[CI][Bugfix] Make test_gpt2_cache_hit observable across V1 EngineCore (#42037)

Signed-off-by: haosdent <[email protected]>

tests/compile/test_aot_compile.py

@@ -1,9 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import functools
 import hashlib
-import multiprocessing
 import os
 import pickle
 import tempfile
@@ -15,7 +13,6 @@ import pytest
 import torch
 
 import vllm.envs as envs
-import vllm.model_executor.layers.activation
 from vllm.compilation.backends import VllmBackend
 from vllm.compilation.caching import (
     StandaloneCompiledArtifacts,
@@ -476,64 +473,57 @@ def test_standalone_compile_correctness():
 @create_new_process_for_each_test("spawn")
 def test_gpt2_cache_hit(monkeypatch: pytest.MonkeyPatch):
     """
-    Test that compiling gpt2 twice results in a cache hit and
-    capture torch dynamic symbol creations to ensure make_symbol
-    not called on cache hit.
-    """
+    Test that compiling gpt2 twice results in a cache hit.
 
-    import torch.fx.experimental.symbolic_shapes as symbolic_shapes_module
-    from torch.utils._sympy.symbol import make_symbol
+    Counter values are read from the EngineCore subprocess via
+    ``LLM.collective_rpc`` so the test works under default V1
+    multiprocessing (no shared memory between test and engine).
+    """
 
     from vllm import LLM
 
-    create_symbol_counter = multiprocessing.Value("i", 0)
-    original_make_symbol = make_symbol
+    def _snap(self):
+        from vllm.compilation.counter import compilation_counter
 
-    @functools.wraps(original_make_symbol)
-    def counting_make_symbol(prefix, idx, **kwargs):
-        with create_symbol_counter.get_lock():
-            create_symbol_counter.value += 1
-        return original_make_symbol(prefix, idx, **kwargs)
+        return (
+            compilation_counter.num_aot_compiles,
+            compilation_counter.num_aot_artifacts_saved,
+            compilation_counter.num_aot_artifacts_loaded,
+        )
 
-    symbolic_shapes_module.make_symbol = counting_make_symbol
-    try:
-        with monkeypatch.context() as m, tempfile.TemporaryDirectory() as tmpdirname:
-            m.setenv("VLLM_CACHE_ROOT", tmpdirname)
-            m.setenv("VLLM_USE_AOT_COMPILE", "1")
-            # First compilation - initialize model and generate
-            llm_model = LLM(
-                model="gpt2",
-                compilation_config=CompilationConfig(
-                    mode=CompilationMode.VLLM_COMPILE,
-                ),
-                max_model_len=256,
-            )
+    # collective_rpc(callable) requires pickle-based serialization.
+    monkeypatch.setenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "1")
 
-            llm_model.generate("Hello, my name is")
-            assert create_symbol_counter.value == 2
-            create_symbol_counter.value = 0
+    with monkeypatch.context() as m, tempfile.TemporaryDirectory() as tmpdirname:
+        m.setenv("VLLM_CACHE_ROOT", tmpdirname)
+        m.setenv("VLLM_USE_AOT_COMPILE", "1")
+        # First compilation - initialize model and generate
+        llm_model = LLM(
+            model="gpt2",
+            compilation_config=CompilationConfig(
+                mode=CompilationMode.VLLM_COMPILE,
+            ),
+            max_model_len=256,
+        )
 
-            # Clean up first model
-            del llm_model
-            disable_envs_cache()
-            vllm.model_executor.layers.activation._ACTIVATION_REGISTRY._dict.clear()
+        llm_model.generate("Hello, my name is")
+        assert llm_model.collective_rpc(_snap)[0] == (1, 1, 0)
 
-            # Second compilation - should hit cache
-            m.setenv("VLLM_FORCE_AOT_LOAD", "1")
-            llm_model = LLM(
-                model="gpt2",
-                compilation_config=CompilationConfig(
-                    mode=CompilationMode.VLLM_COMPILE,
-                ),
-                max_model_len=256,
-            )
-            llm_model.generate("Hello, my name is")
+        # Clean up first model
+        del llm_model
+        disable_envs_cache()
 
-            assert create_symbol_counter.value == 0
-
-    finally:
-        # Restore original method
-        symbolic_shapes_module.make_symbol = original_make_symbol
+        # Second compilation - should hit cache
+        m.setenv("VLLM_FORCE_AOT_LOAD", "1")
+        llm_model = LLM(
+            model="gpt2",
+            compilation_config=CompilationConfig(
+                mode=CompilationMode.VLLM_COMPILE,
+            ),
+            max_model_len=256,
+        )
+        llm_model.generate("Hello, my name is")
+        assert llm_model.collective_rpc(_snap)[0] == (0, 0, 1)
 
 
 @pytest.mark.skipif(not is_torch_equal_or_newer("2.10.0"), reason="requires torch 2.10")